1. Field
Apparatuses and methods consistent with exemplary embodiments relate to an apparatus for processing a medical image and a method of processing a medical image thereof, and more particularly, to a computed tomography (CT) image processing apparatus for obtaining a cross-sectional image reconstructed by using a plurality of graphics processor units (GPUs) and a method of processing a CT image thereof.
2. Description of the Related Art
Computed tomography (CT) image processing apparatuses are used to obtain an image of the internal structure of an object. The CT image processing apparatuses are non-invasive and enable users to view an image of an object after capturing and processing the image including structural details of a body, the internal organs, the flow of body fluids, etc. Users, including doctors, may diagnose medical conditions and diseases by using images generated by the CT image processing apparatuses.
A CT image processing apparatus needs to quickly process a massive amount of data during a reconstruction process in which the CT image processing apparatus obtains a cross-sectional image based on data acquired through CT imaging. Therefore, a CT image processing apparatus executes a task of processing an image, which involves a massive amount of data, by using a field programmable gate array (FPGA) and a multi-central processing unit (Multi-CPU).
In addition, diagnoses using CT-imaging technology are frequently used in emergency situations, compared to types of diagnoses using other medical equipment. Therefore, there may be circumstances in which users need to monitor the medical conditions of a patient in real time while the CT imaging is being performed. Therefore, methods are introduced to reconstruct the cross-sectional images by using graphics processor units (GPUs) which have more enhanced processing capacities than conventional central processing units (CPUs).
Furthermore, the CT image processing apparatuses may boost efficiency by using a Multi-GPU architecture, which includes a plurality of GPUs, when reconstructing a cross-sectional image, to further boost image reconstruction speed.
However, the plurality of GPUs included as part of the Multi-GPU architecture perform the task of an image processing interdependently. Therefore, when the GPUs are used in the reconstruction of the cross-sectional image, if at least one of the plurality of the GPUs malfunctions, the CT image processing apparatuses may have difficulty in reconstructing an intended cross-sectional image.